With progress of science and technology, automobiles and navigation are more and more popular in people's life, and obstacle detection based on vision is widely applied in the fields such as automobile assistant driving and robot navigation. Sensors for detecting an obstacle include, for example, a monocular camera, a stereo camera, a laser sensor, a radar sensor and an infrared sensor. The monocular camera has a broad application prospect in the field of obstacle detection due to its advantages such as convenient installation, small volume and low cost.
There are many methods for detecting an obstacle based on a monocular camera, such as a detection method based on an appearance feature. In the detection method based on the appearance feature, a classifier is trained based on features such as texture, color, edge, symmetry or shape of the obstacle for detection.
The above method is only for detecting an obstacle of a particular type, such as a pedestrian or a vehicle. Multiple classifiers are needed if obstacles of multiple types are detected and the process for training multiple classifiers is tedious and complicated. Therefore, the efficiency is low and the detection is not accuracy for the above conventional method.